Predictive skill of North American Multi‐Model Ensemble seasonal forecasts for the climate rainfall over Central Africa - Tchinda Feudjio, Armand; Tanessong, Roméo Stève; MAMADOU, Ossénatou (My University, 2022)
This study evaluates the predictive performance of the North American Multi-model Ensemble (NMME) over Central Africa (CA) using the historical rainfall data. The African Rainfall Climatology Version 2 (ARC2) is used as a substitute for reference observational data to examine the capability of 11 NMME and their NMME ensemble mean (MME) in simulating rainfall. Using the Kling-Gupta efficiency (KGE), Taylor skill score (TSS), and Heidke skill score, the predictive evaluation of the models is performed from lead 0 to lead 5 of each season. The results show that the NMME models satisfactorily reproduce the bimodal and unimodal structure of rainfall in CA at the lead 0 level of different seasons: December–February (DJF), March–May (MAM), June–August (JJA), and September–November (SON). The pattern correlation coefficient (PCC) shows values of NMME and MME greater than ~0.69 and TSS > 0.60 for all four seasons. The MME presents a maximum in DJF between 0 and 1 month lead time. With the same time scale, just over of the NMME have a KGE between 0 and 0.42. It follows that as the forecast lead time increases, the PCC and TSS of each model become small, with PCC in JJA and DJF, TSS < 0.21 in JJA at lead 5. The NMME models exhibit an important rainfall bias and the calculated scores show the quality of the forecast decreases with increasing lead time; this may justify a constraint on the models to keep the good quality of the long-term seasonal forecast in CA.
Keywords: lead time; NMME; rainfall; seasonal forecast; skill; Taylor skill score